




'''simple docstring'''


import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


UpperCAmelCase_					=   getLogger(__name__)


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]					,       a_						:				str					,       a_						:				str					,       a_						:				int = 8					,       a_						:				int = 1024					,       a_						:				Optional[Any]="val"					,       a_						:				List[Any]=None					,       a_						:				Optional[Any]=False					,       a_						:				List[str]="summarization"					,       a_						:				int=None					,       a_						:				Optional[int]=1					,       a_						:				Dict = None					,       a_						:				List[str]=""					,       **a_						:				int					,       ):
  __a								=       str(a_      )
  assert local_rank is not None
  torch.distributed.init_process_group(backend='nccl'					,       rank=a_      )

  __a								=       Path(a_      )
  __a								=       save_dir.joinpath(f"rank_{local_rank}_output.json"      )
  torch.cuda.set_device(a_      )
  __a								=       AutoModelForSeqaSeqLM.from_pretrained(a_      ).cuda()
  if fpaa:
    __a								=       model.half()
  # determine if we need to increase num_beams
  use_task_specific_params(a_					,       a_      )  # update config with task specific params
  __a								=       generate_kwargs.pop('num_beams'					,       model.config.num_beams      )  # AttributeError risk?
  if num_return_sequences > num_beams:
    __a								=       num_return_sequences

  __a								=       AutoTokenizer.from_pretrained(a_      )
  logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"      )  # if this is wrong, check config.model_type.

  if max_source_length is None:
    __a								=       tokenizer.model_max_length
  if prefix is None:
    __a								=       prefix or getattr(model.config					,       'prefix'					,       ''      ) or ''
  __a								=       SeqaSeqDataset(
      a_					,       a_					,       a_					,       max_target_length=1024					,       type_path=a_					,       n_obs=a_					,       prefix=a_					,       **a_					,       )
  # I set shuffle=True for a more accurate progress bar.
  # If all the longest samples are first, the prog bar estimate is too high at the beginning.
  __a								=       ds.make_sortish_sampler(a_					,       distributed=a_					,       add_extra_examples=a_					,       shuffle=a_      )
  __a								=       DataLoader(a_					,       sampler=a_					,       batch_size=a_					,       collate_fn=ds.collate_fn      )
  __a								=       []
  for batch in tqdm(a_      ):
    __a								=       model.generate(
        input_ids=batch['input_ids'].to(model.device      )					,       attention_mask=batch['attention_mask'].to(model.device      )					,       num_return_sequences=a_					,       num_beams=a_					,       **a_					,       )
    __a								=       tokenizer.batch_decode(a_					,       skip_special_tokens=a_					,       clean_up_tokenization_spaces=a_      )
    __a								=       batch['ids']
    if num_return_sequences > 1:
      __a								=       chunks(a_					,       a_      )  # batch size chunks, each of size num_return_seq
    for i, pred in enumerate(a_      ):
      results.append({'pred': pred, 'id': ids[i].item()}      )
  save_json(a_					,       a_      )
  return results, sampler.num_replicas


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       argparse.ArgumentParser(
      epilog='Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'      )
  parser.add_argument('--data_dir'					,       type=a_					,       help='like cnn_dm/test.source'      )
  parser.add_argument(
      '--model_name'					,       type=a_					,       help='like facebook/bart-large-cnn,t5-base, etc.'					,       default='sshleifer/distilbart-xsum-12-3'					,       )
  parser.add_argument('--save_dir'					,       type=a_					,       help='where to save'					,       default='tmp_gen'      )
  parser.add_argument('--max_source_length'					,       type=a_					,       default=a_      )
  parser.add_argument(
      '--type_path'					,       type=a_					,       default='test'					,       help='which subset to evaluate typically train/val/test'      )
  parser.add_argument('--task'					,       type=a_					,       default='summarization'					,       help='used for task_specific_params + metrics'      )
  parser.add_argument('--bs'					,       type=a_					,       default=8					,       required=a_					,       help='batch size'      )
  parser.add_argument(
      '--local_rank'					,       type=a_					,       default=-1					,       required=a_					,       help='should be passed by distributed.launch'      )

  parser.add_argument(
      '--n_obs'					,       type=a_					,       default=a_					,       required=a_					,       help='How many observations. Defaults to all.'      )
  parser.add_argument(
      '--num_return_sequences'					,       type=a_					,       default=1					,       required=a_					,       help='How many sequences to return'      )
  parser.add_argument(
      '--sync_timeout'					,       type=a_					,       default=600					,       required=a_					,       help='How long should master process wait for other processes to finish.'					,       )
  parser.add_argument('--src_lang'					,       type=a_					,       default=a_					,       required=a_      )
  parser.add_argument('--tgt_lang'					,       type=a_					,       default=a_					,       required=a_      )
  parser.add_argument(
      '--prefix'					,       type=a_					,       required=a_					,       default=a_					,       help='will be added to the begininng of src examples'      )
  parser.add_argument('--fp16'					,       action='store_true'      )
  parser.add_argument('--debug'					,       action='store_true'      )
  __a								=       time.time()
  __a						,							__a								=       parser.parse_known_args()
  __a								=       parse_numeric_n_bool_cl_kwargs(a_      )
  if generate_kwargs and args.local_rank <= 0:
    print(f"parsed the following generate kwargs: {generate_kwargs}"      )
  __a								=       Path(args.save_dir + '_tmp'      )
  Path(a_      ).mkdir(exist_ok=a_      )  # this handles locking.
  __a								=       list(json_save_dir.glob('rank_*.json'      )      )
  if intermediate_files:
    raise ValueError(f"Found files at {json_save_dir} please move or remove them."      )
    # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
  __a								=       {}
  if args.src_lang is not None:
    __a								=       args.src_lang
  if args.tgt_lang is not None:
    __a								=       args.tgt_lang

  Path(args.save_dir      ).mkdir(exist_ok=a_      )
  __a						,							__a								=       eval_data_dir(
      args.data_dir					,       a_					,       args.model_name					,       type_path=args.type_path					,       bs=args.bs					,       fpaa=args.fpaa					,       task=args.task					,       local_rank=args.local_rank					,       n_obs=args.n_obs					,       max_source_length=args.max_source_length					,       num_return_sequences=args.num_return_sequences					,       prefix=args.prefix					,       dataset_kwargs=a_					,       **a_					,       )

  if args.local_rank <= 0:
    __a								=       Path(args.save_dir      )
    save_dir.mkdir(exist_ok=a_      )
    __a								=       gather_results_from_each_node(a_					,       a_					,       args.sync_timeout      )
    __a								=       combine_partial_results(a_      )
    if args.num_return_sequences > 1:
      __a								=       save_dir.joinpath('pseudolabel_results.json'      )
      print(f"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"      )
      save_json(a_					,       a_      )
      return
    __a								=       Path(args.data_dir      ).joinpath(args.type_path + '.target'      )
    with open(a_      ) as f:
      __a								=       [x.rstrip() for x in f.readlines()][: len(a_      )]

    # Calculate metrics, save metrics,  and save _generations.txt
    __a								=       'translation' in args.task
    __a								=       calculate_bleu if calc_bleu else calculate_rouge
    __a								=       'bleu' if calc_bleu else 'rouge'
    __a								=       score_fn(a_					,       a_      )
    __a								=       len(a_      )
    __a								=       time.time() - start_time
    __a								=       round(runtime / metrics['n_obs']					,       4      )
    __a								=       num_replicas
    # TODO(@stas00): add whatever metadata to metrics
    __a								=       save_dir.joinpath(f"{args.type_path}_{metric_name}.json"      )
    save_json(a_					,       a_					,       indent=a_      )
    print(a_      )
    write_txt_file(a_					,       save_dir.joinpath(f"{args.type_path}_generations.txt"      )      )
    if args.debug:
      write_txt_file(a_					,       save_dir.joinpath(f"{args.type_path}.target"      )      )
    else:
      shutil.rmtree(a_      )


def     SCREAMING_SNAKE_CASE							(   a_						:				Dict      ):
  __a								=       []
  for partial_result in partial_results:
    records.extend(a_      )
  __a								=       sorted(a_					,       key=lambda a_      : x["id"]      )
  __a								=       [x['pred'] for x in records]
  return preds


def     SCREAMING_SNAKE_CASE							(   a_						:				Any					,       a_						:				Tuple					,       a_						:				Optional[int]      ):
  # WAIT FOR lots of .json files
  __a								=       time.time()
  logger.info('waiting for all nodes to finish'      )
  __a								=       None
  while (time.time() - start_wait) < timeout:
    __a								=       list(save_dir.glob('rank_*.json'      )      )
    if len(a_      ) < num_replicas:
      continue
    try:
      # make sure all json files are fully saved
      __a								=       lmap(a_					,       a_      )
      return json_data
    except JSONDecodeError:
      continue
  else:
    raise TimeoutError('Rank 0 gave up on waiting for other processes'      )
  # Unreachable


if __name__ == "__main__":
  # Usage for MT:
  run_generate()









'''simple docstring'''


import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase_					=   logging.getLogger()


def     SCREAMING_SNAKE_CASE							(   a_						:				Path					,       a_						:				list      ):
  __a								=       '\n'.join(a_      )
  Path(a_      ).open('w'      ).writelines(a_      )


UpperCAmelCase_					=   "patrickvonplaten/t5-tiny-random"
UpperCAmelCase_					=   "sshleifer/bart-tiny-random"
UpperCAmelCase_					=   "sshleifer/tiny-mbart"

UpperCAmelCase_					=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





class  __lowercase    (	__magic_name__			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()
     __a								=       [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
     _dump_articles(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       str(Path(self.get_auto_remove_tmp_dir()   ) / 'scores.json'   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       run_generate()
       assert Path(UpperCamelCase   ).exists()
       # os.remove(Path(output_file_name))







   def 		UpperCamelCase__      (						self   )					->     int:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([BART_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Union[str, Any]:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([T5_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()

     __a								=       {
         'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
         'de': [
             'Maschinelles Lernen ist großartig, oder?',
             'Ich esse gerne Bananen',
             'Morgen ist wieder ein toller Tag!',
         ],
     }

     __a								=       Path(self.get_auto_remove_tmp_dir()   )
     __a								=       str(tmp_dir / 'scores.json'   )
     __a								=       str(tmp_dir / 'val.target'   )
     _dump_articles(UpperCamelCase   ,				text['en']   )
     _dump_articles(UpperCamelCase   ,				text['de']   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {str(UpperCamelCase   )}\n            {str(UpperCamelCase   )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
     testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']   )

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       with CaptureStdout() as cs:
         run_search()
       __a								=       [' num_beams | length_penalty', model, 'Best score args']
       __a								=       ['Info']
       if "translation" in task:
         expected_strings.append('bleu'   )
       else:
         expected_strings.extend(UpperCamelCase   )
       for w in expected_strings:
         assert w in cs.out
       for w in un_expected_strings:
         assert w not in cs.out
       assert Path(UpperCamelCase   ).exists()
       os.remove(Path(UpperCamelCase   )   )



